Our LATEST PRODUCTS

Paper: Learning to Pour

Pouring is a simple task people perform daily. It is the second most frequently executed motion in cooking scenarios, after pick-and-place. We present a pouring trajectory generation approach, which uses force feedback from the cup to determine the future velocity of pouring. The approach uses recurrent neural networks as its building blocks. We collected the pouring demonstrations which we used for training. To test our approach in simulation, we also created and trained a force estimation system. The simulated experiments show that the system is able to generalize to single unseen element of the pouring characteristics.

Paper: An Approach for Automated Multimodal Analysis of Infants’ Pain

In the paper, we propose an automated multimodal approach that utilizes a combination of both behavioral and physiological pain indicators to assess infants’ pain. We also present a unimodal approach that depends on a single pain indicator for assessment.

In one embodiment, a method for projecting images on a subject includes determining a pose and position of the subject, adjusting a three-dimensional model of an anatomical structure of the subject to match the determined pose and position, and projecting an image of the anatomical structure onto the subject in registration with the actual anatomical structure of the subject to illustrate the location of the structure on or within the subject.

Paper: Recent Datasets on Object Manipulation: A Survey

In the paper, we take a significant step forward by
reviewing datasets that were published in the last 10 years and
that are directly related to object manipulation and grasping.
We report on modalities, activities, and annotations for each
individual dataset and we discuss our view on its use for object
manipulation. We also compare the datasets and summarize
them. Finally, we conclude the survey by providing suggestions
and discussing the best practices for the creation of new datasets.

Paper: Functional Object-Oriented Network for Manipulation Learning

This paper presents a novel structured knowledge representation called functional object-oriented network (FOON) to model the connectivity of the functional-related objects and their motions in manipulation tasks. The graphical model FOON is learned by observing object state change and human manipulations with the objects.

Patent: Systems and Methods for Planning a Robot Grasp Based upon a Demonstration Grasp (US patent ＃9,321,176)

In one embodiment, planning a robot grasp of an object includes determining a grasp type that would be used by a human being to grasp the object, determining a position and orientation of the human being's thumb relative to the object, and planning the robot grasp based upon the determined grasp type and thumb position and orientation.